부산대학교 도서관

A fixed split in the second heart sound (HS) ( ${S}_{{2}}$ ) indicates an aortic septal defect (ASD). This work aims at the shortcomings of using the time interval ( ${T}_{{A_{{2}}}\rightarrow {P_{{2}}}}$ ) between the sounds produced by the aortic valve closure ( ${A}_{{2}}$ ), as well as the pulmonary valve closure ( ${P}_{{2}}$ ) to evaluate the fixed split ${S}_{{2}}$ , which is ${T}_{{A_{{2}}}\rightarrow {P_{{2}}}}$ easily influenced by the heartbeat and difficult to distinguish from other types of splits ${S}_{{2}} $ without considering the entire respiratory phase and the third HS ( ${S}_{{3}}$ ). Thus, this study proposes a novel methodology for detecting an ASD using estimated split coefficients of ${S}_{{2}}$ ( ${S_{{2}}}_{\text {split}}$ ) combined with the number ( ${N}_{S_{{2}}}$ ) of ${S}_{{2}}$ covered by the moving window with 9 s. The important contributions are highlighted as follows: 1) a novel, simple, and efficient methodology based on the Gaussian mixture model (GMM) is proposed for estimating the sounds of ${A}_{{2}}$ and ${P}_{{2}}$ ; 2) an overlapping rate (OLR)-based estimated ${S_{{2}}}_{\text {split}}$ is proposed to overcome the split degree influenced by the heartbeat when using the parameter ${T}_{{A_{{2}}}\rightarrow {P_{{2}}}}$ ; 3) to achieve high precision of the wide splitting of ASDs, the statistics of ${N}_{S_{{2}}}-{S_{{2}}}_{\text {split}}$ and ${N}_{S_{{2}}}-{T}_{{A_{{2}}}\rightarrow {P_{{2}}}}$ are proposed for analysis; and 4) to avoid drawing a unilateral conclusion, the correct ratio (CR) is proposed to predict an ASD. The criteria, derived based on the combination of $\mu _{_{N_{S_{{2}}}, {S_{{2}}}_{\text {split}}}}+\sigma _{_{N_{S_{{2}}}, {S_{{2}}}_{\text {split}}}}\geq 0.45$ , $\mu _{_{N_{S_{{2}}}, {S_{{2}}}_{\text {split}}}}\geq 0.6$ , $\sigma _{_{N_{S_{{2}}}, {S_{{2}}}_{\text {split}}}}\leq 0.01$ , $0.05\leq \mu _{_{N_{S_{{2}}}, {T}_{{A_{{2}}}\rightarrow {P_{{2}}}}}}\leq 0.1$ , and $\text {CR}\geq 0.9$ , are determined to detect ASD. The performance evaluation, as opposed to the state-of-the-art algorithms, demonstrates that the overall accuracy, ${F}1$ score. and Cohen’s kappa coefficient achieved by the proposed algorithm all exceed up to 3% compared with the second highest performance metrics.